---
title: "2017 NYC Health Inspections"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(plotly)
library(p8105.datasets)
library(lubridate)
```

Column {data-width=350}
-----------------------------------------------------------------------


```{r}
data("rest_inspec")
rest_inspec =
  rest_inspec %>%
  filter(grade %in% c("A", "B", "C"), boro != "Missing", inspection_date >= as.Date("2017-01-01")) %>% 
  mutate(boro = str_to_title(boro),
         dba = str_to_title(dba)) 
view(rest_inspec)
```

### Chart A

```{r}

#Which Pizzarias (see points) have FRSA related bugs, and what grades are they getting?

box_ggplot = 
 rest_inspec %>% 
   filter(str_detect(violation_description, "FRSA")) %>% 
  #  distinct(camis, .keep_all = TRUE) %>% 
  # #Businesses can be visited more than once in a year. Should only count once, otherwise you can have more "Fly sightings" than restaurants, if many have more than one occurence..
   mutate(boro = fct_infreq(boro)) %>%
   ggplot(aes(x = boro, fill = grade)) + 
    labs(
    title = "Do flies care about letter grade? Filth fly sightings by Borough",
    y = "Number of sightings",
    x = "Borough") +
   geom_bar() 

ggplotly(box_ggplot)

```

### Chart B


```{r}
rest_inspec %>% 
  filter(score >= 0) %>%
  mutate(text_label = str_c("Health Score: ", score, "\nGrade: ", grade, "\nBusiness:", dba)) %>% 
  plot_ly(
    y = ~score, color = ~boro, type = "box", text = ~text_label, alpha = 0.5) %>% layout(title = 'Health Score by borough (lower is better)')

#Average grade is an A, and businesses are retested until they approach an A, thus all boroughs have similar statistics.
```

### Chart C

```{r}
# rest_inspec %>% 
#   #filter(str_detect(violation_description, "Evidence of")) %>%
#   filter(score >= 0) %>%
#   mutate(text_label = str_c("Health Score: ", score, "\nGrade: ", grade, "\nBusiness:", dba)) %>% 
#   plot_ly(
#     x = ~cuisine_description, y = ~score, type = "scatter", mode = "markers",
#     color = ~score, text = ~text_label, alpha = 0.5) %>% layout(title = 'Health Score by Type of cuisine')

```

```{r}
p <- rest_inspec %>% 
  # count(boro) %>% 
  # mutate(boro = fct_reorder(boro, n)) %>% 
  #mutate(text_label = str_c("Health Score: ", score, "\nGrade: ", grade, "\nBusiness:", dba)) %>%
  ggplot(aes(x=inspection_date, y=score, color = grade)) +
  geom_line() + 
  xlab("") +
  theme(axis.text.x=element_text(angle=60, hjust=1)) +
    labs(
    title = "Scores over time",
    y = "Score",
    x = "Month")

ggplotly(p)


```




```{r}
rest_inspec %>%   mutate(text_label = str_c("Health Score: ", score, "\nGrade: ", grade, "\nBusiness:", dba)) %>%
  filter(str_detect(violation_description, "Evidence of")) %>%
  filter(str_detect(dba, "[Pp][Ii][Zz][Zz][Aa]")) %>%
   mutate(boro = fct_infreq(boro)) %>%
    plot_ly(
    x = ~cuisine_description, y = ~score, type = "scatter", mode = "markers",
    color = ~grade, text = ~text_label, alpha = 0.5)

```


```{r}
# rest_inspec %>%   mutate(text_label = str_c("Health Score: ", score, "\nGrade: ", grade, "\nBusiness:", dba)) %>% 
#   filter(str_detect(dba, "[Pp][Ii][Zz][Zz][Aa]")) %>%
#    #mutate(boro = fct_infreq(boro)) %>%
#     plot_ly(
#     x = ~cuisine_description, y = ~score, type = "box", mode = "markers",
#     color = ~grade, text = ~text_label, alpha = 0.5)

```